English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Computational approaches to schizophrenia: A perspective on negative symptoms

Deserno, L., Heinz, A., & Schlagenhauf, F. (2017). Computational approaches to schizophrenia: A perspective on negative symptoms. Schizophrenia Research, 186, 46-54. doi:10.1016/j.schres.2016.10.004.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-894F-E Version Permalink: http://hdl.handle.net/21.11116/0000-0003-BB1D-B
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Deserno, Lorenz1, 2, 3, Author              
Heinz, Andreas1, 2, Author
Schlagenhauf, Florian1, 2, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Charité University Medicine Berlin, Germany, ou_persistent22              
3Department of Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics, University of Leipzig, Germany, ou_persistent22              

Content

show
hide
Free keywords: Schizophrenia; Decision-making; Reinforcement learning; Computational modelling; Computational psychiatry; Negative symptoms
 Abstract: Schizophrenia is a heterogeneous spectrum disorder often associated with detrimental negative symptoms. In recent years, computational approaches to psychiatry have attracted growing attention. Negative symptoms have shown some overlap with general cognitive impairments and were also linked to impaired motivational processing in brain circuits implementing reward prediction. In this review, we outline how computational approaches may help to provide a better understanding of negative symptoms in terms of the potentially underlying behavioural and biological mechanisms. First, we describe the idea that negative symptoms could arise from a failure to represent reward expectations to enable flexible behavioural adaptation. It has been proposed that these impairments arise from a failure to use prediction errors to update expectations. Important previous studies focused on processing of so-called model-free prediction errors where learning is determined by past rewards only. However, learning and decision-making arise from multiple cognitive mechanisms functioning simultaneously, and dissecting them via well-designed tasks in conjunction with computational modelling is a promising avenue. Second, we move on to a proof-of-concept example on how generative models of functional imaging data from a cognitive task enable the identification of subgroups of patients mapping on different levels of negative symptoms. Combining the latter approach with behavioural studies regarding learning and decision-making may allow the identification of key behavioural and biological parameters distinctive for different dimensions of negative symptoms versus a general cognitive impairment. We conclude with an outlook on how this computational framework could, at some point, enrich future clinical studies.

Details

show
hide
Language(s): eng - English
 Dates: 2016-09-222015-09-232016-10-012016-12-132017-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.schres.2016.10.004
PMID: 27986430
Other: Epub 2016
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : -
Grant ID : -
Funding program : -
Funding organization : Max Planck Society
Project name : -
Grant ID : SCHL1969/1-1 ; SCHL 1969/2-1
Funding program : -
Funding organization : German Research Foundation (DFG)

Source 1

show
hide
Title: Schizophrenia Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 186 Sequence Number: - Start / End Page: 46 - 54 Identifier: ISSN: 0920-9964
CoNE: /journals/resource/954925564675